FedMask

Abstract

Recent advancements in deep neural networks (DNN) enabled various mobile deep learning applications. However, it is technically challenging to locally train a DNN model due to limited data on devices like mobile phones. Federated learning (FL) is a distributed machine learning paradigm which allows for model training on decentralized data residing on devices without breaching data privacy. Hence, FL becomes a natural choice for deploying on-device deep learning applications. However, the data residing across devices is intrinsically statistically heterogeneous (i.e., non-IID data distribution) and mobile devices usually have limited communication bandwidth to transfer local updates. Such statistical heterogeneity and communication bandwidth limit are two major bottlenecks that hinder applying FL in practice. In addition, considering mobile devices usually have limited computational resources, improving computation efficiency of training and running DNNs is critical to developing on-device deep learning applications. In this paper, we present FedMask - a communication and computation efficient FL framework. By applying FedMask, each device can learn a personalized and structured sparse DNN, which can run efficiently on devices. To achieve this, each device learns a sparse binary mask (i.e., 1 bit per network parameter) while keeping the parameters of each local model unchanged; only these binary masks will be communicated between the server and the devices. Instead of learning a shared global model in classic FL, each device obtains a personalized and structured sparse model that is composed by applying the learned binary mask to the fixed parameters of the local model. Our experiments show that compared with status quo approaches, FedMask improves the inference accuracy by 28.47% and reduces the communication cost and the computation cost by 34.48X and 2.44X. FedMask also achieves 1.56X inference speedup and reduces the energy consumption by 1.78X.